How to Build an AI-Ready Foundation for Digital Commerce
What enterprise leaders need to know about the systems, structures, and strategies that unlock real AI value in digital commerce.
Several years ago, the scramble was to "move to the cloud."
Leaders who saw the tides turning were already there and reaping the rewards, and laggards who waited were facing the harsh truth that their existing structures couldn’t keep pace with how quickly competitors on the cloud were moving. Pressure mounted and a mass market shift took place.
Now, the coming sea-change is to be "AI-ready."
The pressure here is real. Investors want to hear about AI strategy, boards want results, and teams want to know what new tools are coming, and when. Everywhere you look vendors are updating their pitch decks and — to add to the stress — your competitors are already making headlines.
But "AI-ready" isn’t a feature or a product. It’s a capability. One that rests on the foundation of your architecture, your operations, and your organization’s willingness to evolve.
This isn’t just about keeping up with tech trends. It’s about whether your business can continue to operate efficiently, serve customers in real time, and compete against leaner, AI-enabled or even AI-native rivals.
Here’s what it actually takes to make that foundation real.
You Can’t Layer AI on a Fragile Foundation
Before we talk about new tools, we have to talk about old problems.
Most enterprise commerce organizations still struggle with brittle integrations, siloed data, hardcoded workflows, and monolithic systems that weren’t built for change, let alone intelligent automation. It’s not that these systems can’t eventually work with AI. But the cost, complexity, and coordination required to make that happen is often prohibitive.
The thing many people are already getting wrong about this exciting new tech is that AI isn’t a magic layer that makes bad systems better. It accelerates what your systems are already capable of. If you want AI to work for you, your core capabilities need to work first.
To build the kind of foundation that can support AI—not just now, but as it continues to evolve—you need to invest in flexibility, data integrity, operational clarity, and an organizational culture that embraces change. These principles come together in four key pillars of AI readiness.
The Four Pillars of AI Readiness in Commerce
These pillars aren’t just checkboxes. They represent the infrastructure and mindset shifts needed to operationalize AI in ways that are sustainable and valuable. Each one reinforces the others, and together, they define what it really means to be prepared for AI-driven commerce.
1. Composable Architecture
An AI-enabled stack is a flexible stack. Composable architecture isn’t about buying best-of-breed for the sake of it. It’s about creating an environment where services are decoupled, loosely connected via APIs, and easy to evolve. That matters when AI is evolving daily; you don’t want to be locked into tools you can’t swap out. A composable framework allows teams to test and implement AI services without full replatforms, reducing vendor risk and accelerating time-to-value.
2. Unified, Accessible Data
AI needs data the way engines need fuel. If your data is scattered across systems, poorly modeled, or locked in vendor-specific formats, you’re not going to get the value you expect. Structured, centralized, accessible data is table stakes and often one of the hardest hurdles to clear. Without unified data, personalization falls flat, reporting is unreliable, and decision-making slows— costing both money and customer trust.
3. Workflow Orchestration
No matter how advanced your AI models are, they’re useless without workflows they can enhance or automate. You need clearly defined, adaptable processes that can accommodate machine input and evolve over time. Content workflows are a useful and popular early focus here: think AI-assisted product descriptions, dynamic personalization, or translation pipelines. But the same orchestration principles apply to customer service, merchandising, search, fulfillment, and any other aspect of the business — frontend or backend — that you’ll be adjusting and enhancing with AI.
Poor workflows lead to bottlenecks, burnout, and bloated processes. Orchestrated flows make AI actionable, allowing it to improve speed and reduce repetitive manual work across your operations.
4. Experimentation Culture
AI implementation isn’t a one-time rollout. It’s iterative, contextual, and evolving. To use it effectively, you need the ability to test, learn, and refine continuously. That means not just tooling, but mindset: KPIs that support experimentation, teams that collaborate across functions, and governance models that allow for fast decision-making and feedback. Organizations that embrace experimentation are better equipped to pivot fast, avoid sunk costs, and scale what works.
Evolving the Org: People, Practices, and AI Collaboration
What This Means for You: If your teams can’t ship updates fast, if your data is stuck in silos, if every new tool takes six months to implement, you’re not AI-ready yet. But you can be. Start with flexible architecture, unify your data, clarify your workflows, and empower your teams to adapt.
Laying the foundation with composable systems and strong data is only half the equation. To unlock AI's real value, organizations need to evolve how their teams work and engage their people in the right ways. Ultimately, the rise of AI isn’t just changing technology stacks, it’s changing the relationship between human effort and digital systems.
As AI becomes part of the workflow, especially as agents start to become collaborators in how we build experiences, roles will shift. Content teams, for example, will move from creators to curators, directing AI to generate first drafts and spending more time refining and approving. Merchandisers will rely on decision-support systems to dynamically allocate inventory or pricing. Engineers will focus less on building static experiences and more on configuring adaptive systems.
To make this work, teams need shared language, integrated workflows, and space to experiment together. That means breaking down functional silos and ensuring collaboration between business, tech, data, and experience teams. The most successful organizations will be those that don’t just adopt AI tools, but build AI and agent fluency into their culture.
Ask These Questions Before You Buy Anything With "AI" on the Label
AI is in nearly every vendor pitch today. But before you sign the next contract, use these questions to qualify vendor claims, align your internal expectations, and avoid costly missteps:
- Will this tool integrate easily with the systems we already use?
- Do we have the data foundation needed to power and sustain its capabilities?
- Will our teams be able to adopt this into their existing workflows without major disruption and/or is the disruption necessary, valuable, and something we can train the team through?
- What business outcome does this solution enable that we couldn’t do efficiently before?
- How easy is it to test, tune, or replace if better options emerge?
If you can’t answer those clearly, pause. Like any major technology investment, the wrong AI investment will set you back more than it moves you forward.
Being AI-ready isn’t about proving you’re innovative. It’s about making sure your business can move fast, operate smarter, and adapt in a market that won’t wait.
That starts with a foundation built for flexibility. Commerce organizations that invest there—in composable architecture, unified data, orchestrated workflows, and adaptive teams—won’t just be ready for AI. They’ll be ready for whatever comes next.
Leigh Bryant
Editorial Director, Composable.com
Leigh Bryant is a seasoned content and brand strategist with over a decade of experience in digital storytelling. Starting in retail before shifting to the technology space, she has spent the past ten years crafting compelling narratives as a writer, editor, and strategist.